1. Import cleaned .csv file and look at dataframe.
2. Remove excess columns.
(e.g. pan + crucible data recorded prior to final calcualted weights)
# remove pan, crucible, and sediment combo weights not necessary for analysis
cr.mat = cr.mat %>%
select(-na.rm, -pan_number, -pan_weight, -pan_sed_ww_pre_60C, -pan_sed_dw_post_60C, -pan_sed_dw_pre_450C, -pan_sed_dw_post450C, -crucible_weight, -crucible_sed_dw_pre_950C, -crucible_sed_dw_post_950C, -sed_ww_pre_60C, -sed_dw_post_60C, -sed_dw_pre_450C, -sed_dw_post_450C, -sed_dw_pre_950C, -sed_dw_post_950C)
colnames(cr.mat)
## [1] "X" "otter_region" "site"
## [4] "latitude" "longitude" "core_letter"
## [7] "layer_number" "layer_thickness" "layer_volume"
## [10] "sample_volume" "dry_bulk_density" "om_weight"
## [13] "im_weight" "om_percent" "im_percent"
## [16] "om.im_ratio" "om_density" "im_density"
## [19] "core_depth"
3. Where are the processed cores from?
# new data frame for all cores
locations_all = cr.mat %>%
distinct(cr.mat, latitude, longitude, site) %>%
drop_na()
# map "locations_all"
leaflet(locations_all) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude, popup = ~site, label = ~site)
4. Plotting
All sites

You will notice that site L06 is way different than the rest, so far. This core was loaded with chunky woody debris; it’s real data but an outlier compared to the other sites. Here are two images of the sliced core from site L06:

